To run a prompt against the `gpt-3.5-turbo` model, run this:
```python
import llm
model = llm.get_model("gpt-3.5-turbo")
model.key = 'YOUR_API_KEY_HERE'
response = model.prompt("Five surprising names for a pet pelican")
print(response.text())
```
The `llm.get_model()` function accepts model names or aliases - so `chatgpt` would work here too.
Run this command to see a list of available models and their aliases:
```bash
llm models list
```
If you have set a `OPENAI_API_KEY` environment variable you can omit the `model.key = ` line.
### Models from plugins
Any models you have installed as plugins will also be available through this mechanism, for example to use Google's PaLM 2 model with [llm-palm](https://github.com/simonw/llm-palm)
```bash
pip install llm-palm
```
```python
import llm
model = llm.get_model("palm")
model.key = 'YOUR_API_KEY_HERE'
response = model.prompt("Five surprising names for a pet pelican")
print(response.text())
```
You can omit the `model.key = ` line for models that do not use an API key
A prompt object represents all of the information needed to be passed to the LLM. This could be a single prompt string, but it might also include a separate system prompt, various settings (for temperature etc) or even a JSON array of previous messages.
The `Model` class is an abstract base class that needs to be subclassed to provide a concrete implementation. Different LLMs will use different implementations of this class.
-`prompt(prompt: str, stream: bool, ...options) -> Response` - a convenience wrapper which creates a `Prompt` instance and then executes it. This is the most common way to use LLM models.
-`response(prompt: Prompt, stream: bool) -> Response` - execute a prepared Prompt instance against the model and return a `Response`.
The response from an LLM. This could encapusulate a string of text, but for streaming APIs this class will be iterable, with each iteration yielding a short string of text as it is generated.
Calling `.text()` will return the full text of the response, waiting for the stream to stop executing if necessary.
